Want a career in data science? You should read this.

Why choose a career in Data Science?

Data Science is a field where you can create huge impacts by leveraging some fundamental ideas from mathematics and coding.

Being able to extract key insights from data to help build a better future makes it one of the most fulfilling careers. Often people only associate data science with large tech companies (like amazon, google, facebook), but almost every industry can gain a substantial advantage with data science. Alongside the cool aspects of being able to leverage data to gain key insights, data scientists also commonly enjoy a lucrative career. 

Fun fact: Did you know that a starting salary as a Data Scientist in London is between £35k – £40k? This is 80% higher than the average graduate salary in London!

Top 4 reasons for a career in Data Science

  • Fulfilling work
  • Very interesting technical topics
  • High demand
  • Great pay and big benefits

Data Science is Interdisciplinary

Data Science is Interdisciplinary

There are many skills or disciplines that you’re going to need in order to continue a career path in data science, such as

  • Computer Science: Computer science comprises of skills like knowledge of coding, algorithm and data structures.
  • Math & Statistics: Knowledge of Algebra, Calculus, Statistics and Probability theory.
  • Domain Knowledge: Having expertise in field you’re working in.

Intersection of all these skills is Data Science.

These are lot of skills and it takes lot of time and efforts to learn. So, employers are really looking for what they call T-shaped candidates.

call T-shaped candidates

T-Shaped Candidates have 

  • General knowledge about a lot of these topics( Computer Science, Domain Knowledge, Maths & Statistics etc.) 
  • Expert knowledge about specific domain e.g. expert in coding and data visualisation.

Data Science various positions and titles

The term Data Scientist is not the only job title that a companies use. Different titles for different focuses of the role often appear. Keep in mind that companies also use the same term differently, a Data Scientist at a small company may be called a Data Analyst at another company.

Common titles and general job descriptions:

Product Analyst / Product Data Scientist

  • Analyses user data to create reports for product managers.
  • Typically using coding as main tool, but company may prefer pre-defined software tool (eg tableau).

Business Analyst / Business Intelligence

  • Creating insights and analysis from business data.
  • Often focuses on specific tool such as tableau or excel.

Machine Learning Engineer

  • Very technical role, both theoretical knowledge and good coding skills are necessary.
  • Creates custom machine learning algorithm models for the team.

Data Engineer

  • Technical role focused on coding and tools, not so much theory.
  • Builds pipelines connecting data warehouse to data analysis or machine learning systems.

Data Scientist

  • Requires a mix of coding and theory skills.
  • Often company will post a more detailed use case on the actual job posting.

Overall, for many of these positions, try focusing less on the actual title and focus more on the qualifying skills of the job posting. Websites such as Glassdoor can help you understand and find out more information on common company titles and descriptions.

 

Read also: What I learned from all my data science interviews

Tagged with:

Manish Prasad

An experienced data scientists with huge passion for working on new challenges.

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